WEBVTT 00:00:07.520 --> 00:00:10.557 All right. Good afternoon, everybody. 00:00:10.640 --> 00:00:13.917 Are you all having a good time? 00:00:14.000 --> 00:00:17.837 >> Oh, fantastic. Amazing. Are you ready for Metallica? 00:00:17.920 --> 00:00:20.157 >> Are you ready for Benson Boone? 00:00:20.240 --> 00:00:23.597 >> Raise your hand if you're going to Metallica. 00:00:23.680 --> 00:00:26.957 Awesome. Who's looking forward to Benson Boon? 00:00:27.040 --> 00:00:28.477 >> All right. Well, so we got a It's kind 00:00:28.560 --> 00:00:30.557 of a mix. 00:00:30.640 --> 00:00:36.840 I don't know. Um, please welcome Daario. 00:00:39.520 --> 00:00:44.957 >> Hey, Mark. Thanks for having me. Great to have you, Dario. 00:00:45.040 --> 00:00:48.477 Welcome. >> Thank you. 00:00:48.560 --> 00:00:52.157 >> Uh, we're really excited to have you, Daario, and introduce you to the 00:00:52.240 --> 00:00:55.677 audience and everybody here. And, you know, we've had such a great 00:00:55.760 --> 00:00:57.677 partnership. We've had an opportunity to 00:00:57.760 --> 00:01:01.357 speak so many times and um you know work 00:01:01.440 --> 00:01:04.237 with some customers together and uh also 00:01:04.320 --> 00:01:05.997 build some technology together. It's 00:01:06.080 --> 00:01:08.717 been exciting to use your model inside 00:01:08.800 --> 00:01:11.597 Slack with our new Slackbot. But you 00:01:11.680 --> 00:01:15.757 know, as I've talked to so many of my friends about you, we've gotten to know 00:01:15.840 --> 00:01:20.477 each other, but a lot of people don't know you. So maybe you could tell a 00:01:20.560 --> 00:01:22.717 little folks about a little about 00:01:22.800 --> 00:01:25.037 yourself, kind of your beginnings, where 00:01:25.120 --> 00:01:28.477 you're from, you know, and also kind of 00:01:28.560 --> 00:01:33.277 uh I'll ask you a couple of the core questions along the way, but to take it 00:01:33.360 --> 00:01:37.197 from the start. Yeah, absolutely. So I grew up right here in uh in I grew up 00:01:37.280 --> 00:01:39.437 right here in San Francisco. I was uh 00:01:39.520 --> 00:01:42.637 originally interested in science, not in 00:01:42.720 --> 00:01:45.277 uh not in tech per se. So you know I did 00:01:45.360 --> 00:01:48.397 my did my uh uh graduate work in uh 00:01:48.480 --> 00:01:50.477 neuroscience as a posttock in 00:01:50.560 --> 00:01:55.597 computational biology at at Stanford and you know as I was working hard on 00:01:55.680 --> 00:01:59.517 solving the problems of biology you know trying to understand the diseases we we 00:01:59.600 --> 00:02:02.477 we face like uh like cancer and to 00:02:02.560 --> 00:02:06.877 understand biology one of the things I noticed is that the complexity of 00:02:06.960 --> 00:02:10.637 biology felt like it was beyond human scale you know and I was starting to 00:02:10.720 --> 00:02:14.717 despair a little bit that that we would be able to make progress at any speed 00:02:14.800 --> 00:02:18.477 just because of you know if you've ever seen one of these diagrams of human 00:02:18.560 --> 00:02:20.157 metabolism it's just it's just beyond 00:02:20.240 --> 00:02:23.277 human scale but almost exactly at the 00:02:23.360 --> 00:02:26.077 time that I was wondering about this out 00:02:26.160 --> 00:02:28.637 of Google out of University of Toronto 00:02:28.720 --> 00:02:33.517 came the first work on deep neural networks uh and I looked at it you know 00:02:33.600 --> 00:02:36.637 I I replicated some of the results myself I said my god this stuff really 00:02:36.720 --> 00:02:37.917 works this stuff's really going to go 00:02:38.000 --> 00:02:40.717 there so I went into AI ended up working 00:02:40.800 --> 00:02:43.997 at um uh Google I joined open AI I in 00:02:44.080 --> 00:02:49.597 the early days I led research there for for for several years um uh and and and 00:02:49.680 --> 00:02:53.837 you know was one of the people who kind of documented this trend of exponential 00:02:53.920 --> 00:02:58.077 scaling of the technology um you know myself and the folks who eventually 00:02:58.160 --> 00:02:59.757 became the anthropic co-founders were 00:02:59.840 --> 00:03:03.197 among the first to uh study the scaling 00:03:03.280 --> 00:03:05.277 of the technology and so you know around 00:03:05.360 --> 00:03:08.957 2019 or 2020 had really two realizations 00:03:09.040 --> 00:03:12.637 one was wow AI is is really going to you 00:03:12.720 --> 00:03:16.237 know it's going to It's going to scale to the moon and it's really going to be 00:03:16.320 --> 00:03:18.957 you know this whole thing is going to reshape the economy. It'll take a few 00:03:19.040 --> 00:03:20.557 years but it's going to reshape the 00:03:20.640 --> 00:03:22.317 economy and you know they are almost 00:03:22.400 --> 00:03:26.317 there now. Um uh and and the second was 00:03:26.400 --> 00:03:30.397 look there's you know these systems are different from ordinary software. You 00:03:30.480 --> 00:03:33.677 don't write every line of code right building ordinary software it's like 00:03:33.760 --> 00:03:36.797 building a skyscraper or something. You make the blueprint you make everything 00:03:36.880 --> 00:03:41.837 to design. This is ironically a bit more like biological. It's organic or 00:03:41.920 --> 00:03:43.517 something. You're growing these models. 00:03:43.600 --> 00:03:48.077 You set a high level recipe. It's like growing a plant or baking a cake or 00:03:48.160 --> 00:03:51.277 something like that. And that unpredictability combined with the 00:03:51.360 --> 00:03:55.517 incredible economic impact means that safety and responsibility are going to 00:03:55.600 --> 00:03:59.597 be very important to make sure that the models behave predictably as well as as 00:03:59.680 --> 00:04:03.117 we deploy them and they have enormous impact on the world. We you know we we 00:04:03.200 --> 00:04:06.477 need to make sure that we deploy them responsibly. And so those were kind of 00:04:06.560 --> 00:04:09.117 the two pillars on which myself and the 00:04:09.200 --> 00:04:11.837 seven co-founders um who left OpenAI to 00:04:11.920 --> 00:04:15.357 to found Enthropic um you know generally 00:04:15.440 --> 00:04:17.357 generally based our generally based our 00:04:17.440 --> 00:04:19.997 view uh and you know the the the rest is 00:04:20.080 --> 00:04:22.717 history. Enthropic has been um you know 00:04:22.800 --> 00:04:25.597 around for 4 and 1/2 years. Um we're 00:04:25.680 --> 00:04:30.397 growing very quickly. um you know I think we're we're at this point um by 00:04:30.480 --> 00:04:35.117 revenue have at least a plurality perhaps the majority of uh um you know 00:04:35.200 --> 00:04:39.197 enterprise enterprise customers and enterprise revenue while most of the 00:04:39.280 --> 00:04:42.957 other players are are focused on the consumer side and we find that you know 00:04:43.040 --> 00:04:45.997 as as you know I think you would know from running an enterprise focused 00:04:46.080 --> 00:04:48.797 company these these values of trust and 00:04:48.880 --> 00:04:53.757 responsibility you know are really are synergistic with serving the enterprises 00:04:53.840 --> 00:04:57.357 right these values help us to serve the enterprise and Having a business model 00:04:57.440 --> 00:05:01.277 that serves the enterprise helps to reinforce our values. 00:05:01.360 --> 00:05:03.917 >> One of the one of the things um and you 00:05:04.000 --> 00:05:05.517 went pretty fast into kind of starting 00:05:05.600 --> 00:05:08.557 the company and kind of shot out of uh 00:05:08.640 --> 00:05:11.997 uh uh shot through uh Google and API to 00:05:12.080 --> 00:05:15.837 Anthropic, but you didn't mention your sister and that was like one of the 00:05:15.920 --> 00:05:19.277 things that first really shocked me when we started to get to know you was you 00:05:19.360 --> 00:05:23.437 and your sister together are working on this company and can you kind of tell us 00:05:23.520 --> 00:05:26.637 the origin story? >> Yeah. Yeah. So, you know, there are 00:05:26.720 --> 00:05:30.397 seven co-founders and me and Daniela are are two of them. As I mentioned, we 00:05:30.480 --> 00:05:34.397 both, you know, we both grew up in San Francisco. We went to to LOL High School 00:05:34.480 --> 00:05:37.997 and, you know, even even very early on, we really figured that we wanted to do 00:05:38.080 --> 00:05:41.197 >> My mom went to LOL and she's sitting right there. So, she's going to be very 00:05:41.280 --> 00:05:45.917 happy about that. >> Amazing. Um, uh, no, no, it was it was 00:05:46.000 --> 00:05:49.357 it was a great place. I actually didn't over Daniela was four years younger than 00:05:49.440 --> 00:05:52.557 me, so we were never there at the same year, but I was there for four and then, 00:05:52.640 --> 00:05:56.797 you know, the next four years she was she was there. So um uh yeah you know 00:05:56.880 --> 00:06:01.037 very early on you know we we we had these you know these ambitions to you 00:06:01.120 --> 00:06:04.557 know to to do something to do something good for the world to have some kind of 00:06:04.640 --> 00:06:06.797 grand project and and to work together. 00:06:06.880 --> 00:06:10.957 I think I think many many people you know kind of or many siblings I guess 00:06:11.040 --> 00:06:15.117 have these have these ambitions but you know some somehow things found their way 00:06:15.200 --> 00:06:17.677 to us actually actually being able to do 00:06:17.760 --> 00:06:21.197 it. So um uh you know she joined me at 00:06:21.280 --> 00:06:25.517 at she joined me at OpenAI in uh uh 2018 00:06:25.600 --> 00:06:29.677 um and then the two of us along with some folks we worked with many of whom 00:06:29.760 --> 00:06:32.237 had been longtime uh uh friends decided 00:06:32.320 --> 00:06:36.717 to start Anthropic. So me and Daniela essentially essentially run the company 00:06:36.800 --> 00:06:41.517 together. You know she's responsible for the dayto-day running of the company. 00:06:41.600 --> 00:06:45.597 you know, kind of operate, you know, how how the company is put put together, how 00:06:45.680 --> 00:06:50.157 the org runs, what its processes are, does, you know, kind of kind of a fair 00:06:50.240 --> 00:06:54.397 amount of the the the, you know, kind of the business on the on the the business 00:06:54.480 --> 00:06:59.117 on the structural side. Um, uh, you know, I I I I just, you know, couldn't 00:06:59.200 --> 00:07:02.877 couldn't run the company without her and without her without her doing all those 00:07:02.960 --> 00:07:05.277 things. Um you know I tend to think 00:07:05.360 --> 00:07:07.997 about the strategic side where the where 00:07:08.080 --> 00:07:10.957 the research is going what our kind of 00:07:11.040 --> 00:07:15.197 you know plans are as a company strategically how we think about safety 00:07:15.280 --> 00:07:19.197 how we think about the company's uh uh role in the world and and the vision and 00:07:19.280 --> 00:07:21.997 and I find that's a very good um that's 00:07:22.080 --> 00:07:23.437 a very good split that's a very good 00:07:23.520 --> 00:07:26.477 separation um because both of us can do 00:07:26.560 --> 00:07:30.717 both pieces but it's it's really hard to switch between a kind of operational 00:07:30.800 --> 00:07:34.717 mode and a vision mode and I think both people can be more effective if you if 00:07:34.800 --> 00:07:37.037 you split. >> Thanks for allowing me to come back to 00:07:37.120 --> 00:07:40.957 that because I think it's such a great part of the company and the culture and 00:07:41.040 --> 00:07:45.277 how you're working and last year she was here and now this year you're here. Um 00:07:45.360 --> 00:07:48.237 and we've known you all from the beginning. I mean I think Salesforce 00:07:48.320 --> 00:07:52.477 owns like a some little point or something of anthropics. So we're very 00:07:52.560 --> 00:07:54.717 happy to be an investor. >> Very early investor. Very 00:07:54.800 --> 00:07:56.637 >> very early investor. Believe in you. 00:07:56.720 --> 00:07:59.517 >> I think you I think you invested when the valuation was 4 billion. Is that 00:07:59.600 --> 00:08:01.517 correct? >> I I don't know but it was too high. 00:08:01.600 --> 00:08:04.157 whatever it was. >> I haven't. 00:08:04.240 --> 00:08:05.997 >> Before you say it's too high, you should 00:08:06.080 --> 00:08:07.997 calculate how many times it's gone up since then. 00:08:08.080 --> 00:08:10.877 >> Okay. We made money so far. 00:08:10.960 --> 00:08:15.837 >> Well, um, no, I I'm thrilled obviously that we're part of your journey and, uh, 00:08:15.920 --> 00:08:20.077 you know, we love working with you. We love working with Daniela, um, so much. 00:08:20.160 --> 00:08:24.317 And you know, I think kind of getting back to this story of you and your 00:08:24.400 --> 00:08:26.877 sister and your friends, you know, starting this company here in San 00:08:26.960 --> 00:08:30.477 Francisco and then you came to us, we were starting to consolidate some of our 00:08:30.560 --> 00:08:33.997 real estate here and then you, you know, we had a conversation about you taking 00:08:34.080 --> 00:08:37.277 over the Slack building. We had just bought Slack, >> which we have done. 00:08:37.360 --> 00:08:38.797 >> And now you're in the Slack building. 00:08:38.880 --> 00:08:42.717 So, you're right on the park with us looking at the tower from your window. 00:08:42.800 --> 00:08:46.797 >> I I believe my office is what I I was told it was formerly Steuart's office. 00:08:46.880 --> 00:08:49.597 >> Formerly Steuart's office. Very good. 00:08:49.680 --> 00:08:51.917 And I would just, you know, it just just 00:08:52.000 --> 00:08:53.997 occurred to me like here you are. You're 00:08:54.080 --> 00:08:58.957 the San Franciscan. Um, you're right on you're right on the park with us. You're 00:08:59.040 --> 00:09:03.437 right on the tower with us. We're an investor in the company. Um, it's, you 00:09:03.520 --> 00:09:06.717 know, obviously a critical part of Slack today, right? In terms of how you 00:09:06.800 --> 00:09:10.317 couldn't use Slack without it, but you're at the very bleeding edge of 00:09:10.400 --> 00:09:12.237 these models. It's really incredible. 00:09:12.320 --> 00:09:15.997 And you've really been visionary uh, right from the very beginning. And 00:09:16.080 --> 00:09:19.757 when I didn't know you when you were at OpenAI, I only met you after you started 00:09:19.840 --> 00:09:22.797 the company. So just now kind of take us 00:09:22.880 --> 00:09:27.037 through your expectation. What has been your biggest surprise, 00:09:27.120 --> 00:09:30.637 you know, since it's only been what, four and a half, 5 years since you kind 00:09:30.720 --> 00:09:33.357 of walked out the door, said goodbye to 00:09:33.440 --> 00:09:37.997 Sam, right? And then you just said, "Okay, Daniela, we're going to we're 00:09:38.080 --> 00:09:41.757 going to get this going." What what was your biggest shock right there? Yeah, I 00:09:41.840 --> 00:09:44.317 mean, you know, there were there were so many moments because I think that, you 00:09:44.400 --> 00:09:46.077 know, the last four and a half years 00:09:46.160 --> 00:09:49.117 were were just so surprising overall. 00:09:49.200 --> 00:09:51.597 Um, you know, I think I think at a high 00:09:51.680 --> 00:09:54.397 level there's a dichotomy where on one 00:09:54.480 --> 00:09:59.357 hand, everything we've seen over the last 4 and 1/2 years was in some sense 00:09:59.440 --> 00:10:02.717 predicted by those straight lines on graphs that I made with the scaling 00:10:02.800 --> 00:10:06.717 laws, right? these enormous data center buildouts, the way that our revenue is 00:10:06.800 --> 00:10:11.757 going up, you know, 10x per year, the the use across the economy. You could 00:10:11.840 --> 00:10:15.117 you you know, you could see it all in these scribbles on the page. But 00:10:15.200 --> 00:10:18.477 actually seeing it play out is is wild. 00:10:18.560 --> 00:10:22.797 And and a lot of the individual things that that I'm seeing happening, I just I 00:10:22.880 --> 00:10:24.637 just never would have I never would have 00:10:24.720 --> 00:10:27.437 expected. I guess if I were to pick one 00:10:27.520 --> 00:10:30.797 most surprising thing, it is, you know, 00:10:30.880 --> 00:10:35.677 we left one company and started another company. And uh something you see over 00:10:35.760 --> 00:10:37.437 time is that is that as you build a 00:10:37.520 --> 00:10:40.477 company, you really do see through the 00:10:40.560 --> 00:10:43.437 people you hire, through the choices 00:10:43.520 --> 00:10:47.757 that the company makes, you kind of see the paths diverge and you see 00:10:47.840 --> 00:10:51.997 differentiation, right? OpenAI has gone very much in the consumer direction. 00:10:52.080 --> 00:10:56.157 We've gone very much in the enterprise direction. we've taken, you know, just 00:10:56.240 --> 00:11:00.077 just the way we approach things is is very different. And so you have this 00:11:00.160 --> 00:11:03.357 market structure where there are these two companies that are like it's sort of 00:11:03.440 --> 00:11:06.397 a natural experiment. They're they're just taking a very different approach. 00:11:06.480 --> 00:11:10.717 Both exist in the ecosystem, but but the approach they take is very different. 00:11:10.800 --> 00:11:14.077 >> And then you have to reflect back at your time at Google as well. They're 00:11:14.160 --> 00:11:18.877 also very much with Gemini and, you know, very much a top model as well. How 00:11:18.960 --> 00:11:23.037 do you look at them? Do you look at them as a blend then? Yeah, I mean, you know, 00:11:23.120 --> 00:11:26.877 Google's a partner to us. They're an investor in us. Obviously, their models 00:11:26.960 --> 00:11:31.277 compete with us, compete with us at the same time. But I think this co-optition 00:11:31.360 --> 00:11:34.397 dynamic is becoming more and more common, right? As we have all these 00:11:34.480 --> 00:11:36.397 layers, we have like the chip layer, the 00:11:36.480 --> 00:11:39.757 cloud layer, the the kind of model 00:11:39.840 --> 00:11:43.757 layer, the the like the like go to market and distribution layer, and 00:11:43.840 --> 00:11:47.597 various companies are involved in more than one of these layers. and and so I 00:11:47.680 --> 00:11:52.157 think it's not uncommon for companies to cooperate in one area and and compete in 00:11:52.240 --> 00:11:56.237 another area. It's fairly normal. I think it's also been helped by, you 00:11:56.320 --> 00:11:59.837 know, I I I know quite well some of the folks behind Google. I've been, you 00:11:59.920 --> 00:12:04.237 know, friends with with Demis for uh who, you know, who leads uh who leads 00:12:04.320 --> 00:12:07.997 research at Google for something like 15 years, right? I think he was maybe one 00:12:08.080 --> 00:12:11.997 of the only people who was earlier than me in in seeing the potential of the 00:12:12.080 --> 00:12:16.077 technology. He also has a neuroscience background like me. So I you know I 00:12:16.160 --> 00:12:19.517 think that helps us to get along even as there are some competitive aspects 00:12:19.600 --> 00:12:22.637 between the companies. >> What was your reaction when he won the 00:12:22.720 --> 00:12:27.037 Nobel Prize this year? >> Uh I I was I was very happy for uh 00:12:27.120 --> 00:12:31.037 Demis. Uh uh actually you know I was writing this essay about the potential 00:12:31.120 --> 00:12:35.517 of AI in biology called machines of loving grace. And two days before I was 00:12:35.600 --> 00:12:37.517 about to publish it of course Deis and 00:12:37.600 --> 00:12:40.077 his colleagues Alphafold won the won the 00:12:40.160 --> 00:12:44.557 Nobel Prize. So um uh you know I I I ended up dedicating the essay to the 00:12:44.640 --> 00:12:46.637 2024 you know w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w 00:12:46.720 --> 00:12:47.117 w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w 00:12:47.200 --> 00:12:48.637 w w w w w w w w w w w w w w w w w w w w w w w w w w w w of the Nobel Prize in 00:12:48.720 --> 00:12:52.317 chemistry for for AlphaFold because it was an example of the future that I was 00:12:52.400 --> 00:12:56.077 talking about. >> Very exciting. So just getting back to 00:12:56.160 --> 00:13:00.317 you and Daniela and you're working on and growing the company and up it goes 00:13:00.400 --> 00:13:03.917 and you're doing it right here in San Francisco, you know, what are some of 00:13:04.000 --> 00:13:06.717 the characteristics and the models that you're looking at now that are most 00:13:06.800 --> 00:13:10.637 surprising? What what's really exciting about, you know, as you as you see the 00:13:10.720 --> 00:13:13.597 next next generations of these models unfold? 00:13:13.680 --> 00:13:18.557 >> Yeah. You know, I think broadly it's the jump to being able to do agentic 00:13:18.640 --> 00:13:20.477 abilities. Being able to do things. 00:13:20.560 --> 00:13:21.757 >> What was it? I didn't hear the word. 00:13:21.840 --> 00:13:24.797 >> Oh, sorry. Agentic abilities. Um, uh, 00:13:24.880 --> 00:13:29.837 uh, uh, turning the models into agents that Oh. Oh. Oh. Oh, I see. You've been 00:13:29.920 --> 00:13:35.117 I >> don't worry about it, Dario. Don't worry. I I wasn't. I just wanted I'm a 00:13:35.200 --> 00:13:37.837 little weak in my right ear. genuinely thought you didn't hear. 00:13:37.920 --> 00:13:43.640 >> I see. I understand. >> Um, 00:13:43.760 --> 00:13:46.477 >> uh, yeah, it was very funny, wasn't it? 00:13:46.560 --> 00:13:50.237 >> Definitely a buzz word and you've, you know, perhaps contributed more more to 00:13:50.320 --> 00:13:53.597 mo more than most to to making it to making it a buzz word. But I, you know, 00:13:53.680 --> 00:13:55.117 I think I think it's I think it's a real 00:13:55.200 --> 00:13:58.717 thing, which is being able to do tasks 00:13:58.800 --> 00:14:00.797 end to end in a way that was not 00:14:00.880 --> 00:14:03.277 possible before. And I think we see it 00:14:03.360 --> 00:14:07.277 more uh you know in code than uh you 00:14:07.360 --> 00:14:09.677 know we see it in in any other area. But 00:14:09.760 --> 00:14:11.917 I think code is just a leading indicator 00:14:12.000 --> 00:14:14.717 honestly in other tasks in tasks like 00:14:14.800 --> 00:14:17.277 financial services in tasks like you 00:14:17.360 --> 00:14:22.317 know health and medical in tasks like uh you know insurance or manufacturing many 00:14:22.400 --> 00:14:26.317 of the regulated industries that were working together on our on our on our 00:14:26.400 --> 00:14:31.117 partnership. Agents are agents are just as you know valuable. you just as much 00:14:31.200 --> 00:14:34.797 want to do things end to end, right? You think of the, you know, the sales and go 00:14:34.880 --> 00:14:38.957 to market and, you know, customer management workflows. You want to string 00:14:39.040 --> 00:14:41.837 a bunch of things together and you want the models to pull in a bunch of things 00:14:41.920 --> 00:14:47.277 in the context and learn this, do that, access this information. Um, so it's 00:14:47.360 --> 00:14:50.877 it's just as valuable. It's just the coders are early adopters. So, you know, 00:14:50.960 --> 00:14:54.077 they've they've kind of given us a glimpse of the future because they they 00:14:54.160 --> 00:14:58.397 adopt things so quickly. But I think we're going to see a similar spike in in 00:14:58.480 --> 00:15:02.157 other areas. So I want to ask you I have a few a few 00:15:02.240 --> 00:15:06.077 questions in that area. So the f you know when we first started getting in 00:15:06.160 --> 00:15:09.837 probably I think one of our very first conversations we were starting to talk 00:15:09.920 --> 00:15:12.077 about this theory and also AGI 00:15:12.160 --> 00:15:14.317 especially and what the potential in the 00:15:14.400 --> 00:15:17.117 future and AGI at that time and this is 00:15:17.200 --> 00:15:21.597 kind of now we go back several years was kind of being defined as yeah the model 00:15:21.680 --> 00:15:25.277 is almost kind of recursive it's building itself it's growing it's 00:15:25.360 --> 00:15:28.237 evolving it knows it is coding itself it 00:15:28.320 --> 00:15:32.317 can code and then it's it's enhancing and it's off and going off and you know 00:15:32.400 --> 00:15:36.637 we had Brett Adcock on the stage earlier today and he was talking about how kind 00:15:36.720 --> 00:15:40.717 of like had a scene out of you know we are Bob we are Legion that the robot had 00:15:40.800 --> 00:15:42.397 taken off and was now you know 00:15:42.480 --> 00:15:44.957 self-replicating into the future and you 00:15:45.040 --> 00:15:48.317 know an infinite number of uh you know 00:15:48.400 --> 00:15:52.477 uh robots it was it was incredibly 00:15:52.560 --> 00:15:57.917 compelling would go off and that was that you know to this to that point a 00:15:58.000 --> 00:16:01.517 few years ago when we were talking it was like well now the model is kind of 00:16:01.600 --> 00:16:03.357 building itself It's growing itself. 00:16:03.440 --> 00:16:06.877 It's develop. It didn't turn out exactly like that though, right? Or it's not 00:16:06.960 --> 00:16:09.357 we're not at that point yet, are we? 00:16:09.440 --> 00:16:10.957 >> Yeah. So, you know, I would separate two 00:16:11.040 --> 00:16:14.557 things. There there's I think inevitably as we, you know, talk about AI in the 00:16:14.640 --> 00:16:19.517 future, this kind of sci-fi sheen on on on, you know, the on the whole thing. 00:16:19.600 --> 00:16:22.557 And that's, you know, in a way it's helpful as a metaphor, but also it can 00:16:22.640 --> 00:16:26.557 be distracting because, you know, it just it just kind of conjures everyone's 00:16:26.640 --> 00:16:29.117 mind into this into this kind of uh, you 00:16:29.200 --> 00:16:34.477 know, into this kind of uh, you know, future dreamy mode. Um, uh, but, you 00:16:34.560 --> 00:16:37.757 know, I think there are some things that are real. One thing that's real is that, 00:16:37.840 --> 00:16:40.957 you know, every 3 months we make a model that's smarter than the month than the 00:16:41.040 --> 00:16:44.477 model we made before. And indeed, the models are slowly getting to where 00:16:44.560 --> 00:16:48.077 they're, you know, better than most humans at most things. maybe at some 00:16:48.160 --> 00:16:50.557 point better all humans than all things. 00:16:50.640 --> 00:16:52.797 The other thing I think is real is you 00:16:52.880 --> 00:16:57.597 know the model is not building itself in the sense that like I have a specific 00:16:57.680 --> 00:17:01.597 copy of the model that's like making a specific copy of the the you know the 00:17:01.680 --> 00:17:05.757 the next model you know we don't have our self-replicating you know nanobots 00:17:05.840 --> 00:17:08.237 or robots or or whatever but but what 00:17:08.320 --> 00:17:10.637 absolutely is happening is that within 00:17:10.720 --> 00:17:13.197 anthropic and elsewhere we are using 00:17:13.280 --> 00:17:16.397 claude to help build products on top of 00:17:16.480 --> 00:17:19.357 claude and to help train the next claude 00:17:19.440 --> 00:17:24.157 to help serve serve quad faster. Um, you know, 6 months ago I I made this 00:17:24.240 --> 00:17:28.237 prediction that, you know, in in 6 months 90% of code would be written by 00:17:28.320 --> 00:17:32.317 by by AI models. Some people think that prediction is wrong, but within 00:17:32.400 --> 00:17:35.997 Anthropic and within a number of companies that we work with, that is 00:17:36.080 --> 00:17:40.557 absolutely true. Um now 90 you know >> so you're saying that 90% of all code 00:17:40.640 --> 00:17:43.677 adenthropics being written by the by the model today 00:17:43.760 --> 00:17:46.877 >> on on many teams you know not uniformly every 00:17:46.960 --> 00:17:50.637 >> is that something that you've kind of provided something kind of 00:17:50.720 --> 00:17:54.477 >> something auditable around that or like shown how that's happened. Yeah, the 00:17:54.560 --> 00:17:59.837 teams play an editing and supervisory role, but you know, Claude can both you 00:17:59.920 --> 00:18:03.597 can say write this feature and Claude will do it and the human kind of looks 00:18:03.680 --> 00:18:05.837 over the code or asks for help from 00:18:05.920 --> 00:18:10.957 another another model, another copy of Claude. Um uh the other thing is 00:18:11.040 --> 00:18:14.317 sometimes we have these long running bugs. you know, when we were training 00:18:14.400 --> 00:18:17.997 our our the most recently released Claude, it was like, you know, the 00:18:18.080 --> 00:18:20.317 cluster was broken. There was this bug. 00:18:20.400 --> 00:18:24.397 Engineers were spending a few days on it. And, you know, we just we just went 00:18:24.480 --> 00:18:26.877 to Claude and we said, I don't know, just kind of play around with the 00:18:26.960 --> 00:18:30.157 cluster and see if you can figure out what's wrong. And, you know, Claude 00:18:30.240 --> 00:18:33.997 found this just super obscure bug that the engineers had missed. So, this this 00:18:34.080 --> 00:18:35.917 was one area where it was like Claude is 00:18:36.000 --> 00:18:37.757 like having a very talented teammate. So 00:18:37.840 --> 00:18:41.197 if claude is now running writing 90% of 00:18:41.280 --> 00:18:43.677 all of the code in enthropic do you need 00:18:43.760 --> 00:18:46.077 less engineers if 90% of all the code 00:18:46.160 --> 00:18:48.797 when will it be 95% or 99%. 00:18:48.880 --> 00:18:53.357 >> Yeah. So you know I you know I think people can misinterpret it as you know 00:18:53.440 --> 00:18:57.437 you know like claude writing 90% of the code means we're firing 90% of the 00:18:57.520 --> 00:19:00.717 software engineers. But you know if you're familiar with the principle of 00:19:00.800 --> 00:19:02.397 >> but you have made some very aggressive 00:19:02.480 --> 00:19:09.357 comments around firing executives >> that that so 00:19:09.440 --> 00:19:11.117 um >> I just want to put two and two together 00:19:11.200 --> 00:19:12.797 to get four. Okay. 00:19:12.880 --> 00:19:18.157 >> Um uh so uh um you know if >> one thing I've learned in knowing you 00:19:18.240 --> 00:19:21.197 for so long is I have to come back and kind of like get that qualifying 00:19:21.280 --> 00:19:25.597 question again. Yes. Because you're go you know you you're you're going fast. 00:19:25.680 --> 00:19:30.157 So trying to I'm trying to keep up with you and it's amazing but I want to just 00:19:30.240 --> 00:19:31.917 make sure I understand exactly what you're saying. 00:19:32.000 --> 00:19:33.517 >> Yes, absolutely. Let me let me let me 00:19:33.600 --> 00:19:36.877 take them one by one. So um uh you know 00:19:36.960 --> 00:19:40.237 under comparative advantage if Claude is 00:19:40.320 --> 00:19:43.597 writing 90% of the code what that means 00:19:43.680 --> 00:19:48.397 usually is you need just as many software engineers you might need more 00:19:48.480 --> 00:19:52.717 because they can they can then be more leveraged. they can focus on the 10% 00:19:52.800 --> 00:19:54.797 that's editing the code or writing the 00:19:54.880 --> 00:19:57.677 10% that's the hardest or supervising a 00:19:57.760 --> 00:20:01.997 group of a of AI models. And so what happens is, you know, you can just end 00:20:02.080 --> 00:20:04.397 up being 10 times more productive 00:20:04.480 --> 00:20:07.677 because the remaining 10% is is now a 00:20:07.760 --> 00:20:11.517 complement to the to the AI system that's that's getting much better at at 00:20:11.600 --> 00:20:13.997 getting much better at what it's producing. So So 00:20:14.080 --> 00:20:17.037 >> it's a rebalancing of the work is what you're really saying. 00:20:17.120 --> 00:20:18.317 >> It's it's not a replacement. 00:20:18.400 --> 00:20:21.117 >> It's not a replacement. It's a rebalancing. Oh, okay. 00:20:21.200 --> 00:20:24.717 >> As that 90% goes to 95 99 totally 00:20:24.800 --> 00:20:28.557 automated end to end, there may be some point at which all bets are off. 00:20:28.640 --> 00:20:31.677 >> What's your time frame for that? You It's a pretty big jump there. 00:20:31.760 --> 00:20:34.157 >> It's very hard to say. Um, but you know, 00:20:34.240 --> 00:20:37.837 we're looking at it. We're looking at it closely. We're always looking for ways 00:20:37.920 --> 00:20:41.917 to encourage complimentarity. >> You see that in the short term or the long term? 00:20:42.000 --> 00:20:44.557 >> Um, I you know, >> is that a 5year goal or a one-year goal? 00:20:44.640 --> 00:20:46.477 >> I'm optimistic about 10 year goal. 00:20:46.560 --> 00:20:48.717 >> I'm optimistic about complimentarity in 00:20:48.800 --> 00:20:51.517 the short run. I you know I I I I I do 00:20:51.600 --> 00:20:54.637 worry as we get out to two years 5 years 00:20:54.720 --> 00:20:57.197 that across the economy not that there 00:20:57.280 --> 00:20:59.597 won't be anything for humans to do but 00:20:59.680 --> 00:21:01.917 that there will be enough disruption 00:21:02.000 --> 00:21:04.957 that that we'll have a you know a kind 00:21:05.040 --> 00:21:07.357 of labor disruption that is you know not 00:21:07.440 --> 00:21:09.837 fundamentally different from what we've 00:21:09.920 --> 00:21:12.157 seen with previous technologies but that 00:21:12.240 --> 00:21:17.277 operates faster that is broader across the economy because it relates to all 00:21:17.360 --> 00:21:19.357 kinds. I think that's in the next two to five years. 00:21:19.440 --> 00:21:20.797 >> Two to five years I would say. 00:21:20.880 --> 00:21:22.637 >> Okay. And at what point just coming back 00:21:22.720 --> 00:21:27.997 to my previous question, at what point is Enthropic the model writing 100% of 00:21:28.080 --> 00:21:31.917 the code? >> Yeah. Again, it's it's a tricky question 00:21:32.000 --> 00:21:35.757 because maybe even if the model's writing 100% of the code, the humans are 00:21:35.840 --> 00:21:41.320 still supervising the model or prompting it or clarif 00:21:44.000 --> 00:21:48.317 it's it's it's it's a surprisingly clever thing. I don't you know I I'm I I 00:21:48.400 --> 00:21:52.877 I am concerned particularly about people's ability to adapt as the jobs 00:21:52.960 --> 00:21:57.117 are changing fast jobs are changing so fast but comparative comparative 00:21:57.200 --> 00:21:59.997 advantage is powerful that's going to be one of our tools in this well you've 00:22:00.080 --> 00:22:03.117 mentioned healthcare several times but we've seen these kind of very 00:22:03.200 --> 00:22:07.197 conflicting studies recently in healthcare around accuracy especially 00:22:07.280 --> 00:22:08.797 because these are word models there 00:22:08.880 --> 00:22:10.797 seems to be some level of accuracy 00:22:10.880 --> 00:22:16.237 that's not 100% and we're you know you know and then when applied to some of 00:22:16.320 --> 00:22:19.757 these kind of other things like radiology is one where we saw these very 00:22:19.840 --> 00:22:22.157 interesting, you know, studies where we 00:22:22.240 --> 00:22:26.957 just didn't see the accuracy of a radiologist reflected in what the model 00:22:27.040 --> 00:22:31.037 was doing. Is that how you see it going forward or how did you reflect back on 00:22:31.120 --> 00:22:34.877 that? >> Yeah, I I would say where we are now is 00:22:34.960 --> 00:22:38.397 I think of the models and I think a lot of people who use the models in practice, 00:22:38.480 --> 00:22:40.957 >> you know where I'm going, right, where I'm trying to I'm trying to like 00:22:41.040 --> 00:22:44.237 understand where are we really in the evolution of the world. 00:22:44.320 --> 00:22:48.157 >> Where's the arc? So, so I think a lot of I I think and a lot of the people who 00:22:48.240 --> 00:22:50.477 use the models in practice, I think 00:22:50.560 --> 00:22:53.037 think of the models as maybe a bit like 00:22:53.120 --> 00:22:55.677 a second opinion, right? Where you don't 00:22:55.760 --> 00:23:00.317 necessarily trust them alone. Just like if a doctor says something to me, I you 00:23:00.400 --> 00:23:03.997 know, I don't I don't necessarily take that as fact. They're an expert, but and 00:23:04.080 --> 00:23:08.157 they're often right, but they're not always right. Um uh so, you know, I'll 00:23:08.240 --> 00:23:12.397 tell >> especially in this one category, radiology. It seems strange like it just 00:23:12.480 --> 00:23:15.437 can't get its >> I was going to say can't get its head 00:23:15.520 --> 00:23:18.797 around it but it's not really the right metaphor but doesn't have 00:23:18.880 --> 00:23:22.637 >> it does there's something about either it doesn't have enough data or it's not 00:23:22.720 --> 00:23:25.437 getting it's too nuanced. What is it? 00:23:25.520 --> 00:23:28.877 >> Yeah. So let me let me actually tell you a story about about one case where it 00:23:28.960 --> 00:23:32.717 where it where it did work. Um so you know we're talking about you know 00:23:32.800 --> 00:23:35.997 founding the company with with my sister. She recently had her second 00:23:36.080 --> 00:23:39.917 child while she was pregnant. um you know pregnancy can suppress your immune 00:23:40.000 --> 00:23:44.477 system was was viral. For some reason it didn't occur to to any of them that it 00:23:44.560 --> 00:23:48.957 could be a bacterial infection. She took all her data. She uploaded it to Claude 00:23:49.040 --> 00:23:52.877 and you know Claude you know systematically listed the possibilities 00:23:52.960 --> 00:23:55.277 in the way that the doctors had somehow missed. 00:23:55.360 --> 00:23:58.957 >> That is amazing right? How many of us have already used the models in some way 00:23:59.040 --> 00:24:01.117 to look at our healthcare? Raise your hand. 00:24:01.200 --> 00:24:03.277 >> Yeah I think we're all having exactly that. 00:24:03.360 --> 00:24:04.237 >> That's that's a lot of people. 00:24:04.320 --> 00:24:07.757 >> Yeah it's amazing right? where you can kind of say especially how many of you 00:24:07.840 --> 00:24:13.117 are the hyperchondriacs that are doing that raise your hand. Yeah, I got a few. 00:24:13.200 --> 00:24:16.637 Okay, I know I'm >> but but but here's the point which is 00:24:16.720 --> 00:24:19.917 Claude listed possibilities. If she had 00:24:20.000 --> 00:24:23.197 just blindly taken the advice of Claude, 00:24:23.280 --> 00:24:27.197 that would not have gone well. Um Claude was good for saying, "Hey, have you 00:24:27.280 --> 00:24:31.757 considered this? Have you considered that?" So, so it has this assistive 00:24:31.840 --> 00:24:34.637 augmentative ability, >> especially in regards to words, right? 00:24:34.720 --> 00:24:37.757 is very good on words. >> Words. Yeah. Yeah. I mean, you know, 00:24:37.840 --> 00:24:41.437 this specialized stuff of reading images like I think that's more about 00:24:41.520 --> 00:24:44.957 specialized models like if you don't have the right data, maybe it's not 00:24:45.040 --> 00:24:49.037 doing well. It's less reflective of the >> is that the crux of the issue like in 00:24:49.120 --> 00:24:50.957 radiology cuz like when we're saying 00:24:51.040 --> 00:24:53.437 it's going to improve healthare but we 00:24:53.520 --> 00:24:56.637 also saw this weird study where doctors 00:24:56.720 --> 00:25:01.597 who were using the models some in some cases were relying too much of the 00:25:01.680 --> 00:25:05.997 models and then ending up with bad outcomes. On the other side then we saw 00:25:06.080 --> 00:25:10.237 models that were like trying to read radiology but then were not able to 00:25:10.320 --> 00:25:14.637 achieve accuracy. was like this kind of strange bifurcation between the two 00:25:14.720 --> 00:25:17.837 things. >> Yeah. You know, I think there's a lot 00:25:17.920 --> 00:25:22.397 still left to work out in what the role of the human is and what the role of the 00:25:22.480 --> 00:25:26.877 model is. And of course, that's changing as the models improve rapidly. But, you 00:25:26.960 --> 00:25:29.917 know, I think it's something you often see in new technologies where there's 00:25:30.000 --> 00:25:34.317 this great technology and then these practical issues of exactly how do you 00:25:34.400 --> 00:25:38.717 use it, exactly how do you interact with it, they take surprisingly long to work 00:25:38.800 --> 00:25:40.877 out. And you know if you look back at 00:25:40.960 --> 00:25:43.437 you know the early days when people you 00:25:43.520 --> 00:25:48.077 know kind of you know deployed deployed new technologies I don't know it's it's 00:25:48.160 --> 00:25:50.477 just it's often >> you think this is going to be about 00:25:50.560 --> 00:25:52.317 humans and agents working together. 00:25:52.400 --> 00:25:54.397 >> Yeah. No no no exactly just just the 00:25:54.480 --> 00:25:57.517 collaboration model needs to be worked 00:25:57.600 --> 00:26:01.437 out a bit more than it has in these practical areas. Code is good to point 00:26:01.520 --> 00:26:03.917 to like code happens so fast right? 00:26:04.000 --> 00:26:07.197 developers are early adopters and so 00:26:07.280 --> 00:26:09.357 that cycle we've gone through it in like 00:26:09.440 --> 00:26:14.397 months right you can see people talking in blog posts about oh you know what's 00:26:14.480 --> 00:26:18.397 the best way to use cloud code do I want it to suggest and I make edits or I 00:26:18.480 --> 00:26:22.717 write code and it makes edits and these people riff off each other and you know 00:26:22.800 --> 00:26:25.997 over the course of three or four months they work out what the best way to use 00:26:26.080 --> 00:26:30.397 quad code is I think that same thing is happening in healthcare and radiology 00:26:30.480 --> 00:26:33.917 but because there are a lot of barriers in these industries because they're high 00:26:34.000 --> 00:26:36.557 stakes that that process is working 00:26:36.640 --> 00:26:41.037 itself out much slower. >> So that's really kind of getting down to 00:26:41.120 --> 00:26:44.717 it, right? That it's a collaborative thing and like here's the best way to 00:26:44.800 --> 00:26:46.877 use cloud code. Cloud code is writing 00:26:46.960 --> 00:26:49.437 the code, but here's really the best way 00:26:49.520 --> 00:26:52.317 as a developer now to work on cloud 00:26:52.400 --> 00:26:54.877 code. It's not writing 100% of the code. 00:26:54.960 --> 00:26:58.877 It's writing code now I'm like checking here's here's and now now I'm going to 00:26:58.960 --> 00:27:02.717 have to maintain the code. I'm gonna have to provide all the capabilities, 00:27:02.800 --> 00:27:06.317 the whole infrastructure around the code. Is this the right way to think about it? 00:27:06.400 --> 00:27:07.837 >> I I think that's the right way to think 00:27:07.920 --> 00:27:10.397 about it right now. Um uh you know, as 00:27:10.480 --> 00:27:13.597 as the models get stronger, then the 00:27:13.680 --> 00:27:18.557 collaboration may change to more distant supervision by the human. Um so it's 00:27:18.640 --> 00:27:22.717 it's very hard to say what will happen even 6 months from now. But but I think 00:27:22.800 --> 00:27:26.397 what you've said is a very accurate description of where we are now as of 00:27:26.480 --> 00:27:31.597 you know our our you know as of Sonnet 4.5 which was the latest model released at 00:27:31.680 --> 00:27:34.237 >> which is probably one of the best coders that we've seen right 00:27:34.320 --> 00:27:38.557 >> it it you know every every we you know we have these benchmarks that are like 00:27:38.640 --> 00:27:43.357 you know what fraction of you know a random sample of GitHub PRs can this 00:27:43.440 --> 00:27:47.037 thing write from a description and that's over the last year and a half 00:27:47.120 --> 00:27:49.757 that's gone from like 5% which was 00:27:49.840 --> 00:27:54.797 basically the thing can't code at all to something like you know 77%. 00:27:54.880 --> 00:27:58.957 Uh so it's it's it's really >> and it's also yielding also this whole 00:27:59.040 --> 00:28:03.517 other ecosystem of things on top of it as well like even we showed agent force 00:28:03.600 --> 00:28:07.757 vibes yesterday that I think our customers will benefit from or other you 00:28:07.840 --> 00:28:11.037 know other tools lovable these things have been built on these things right 00:28:11.120 --> 00:28:14.637 has that been a surprise to you to see a whole ecosystem on top of the model 00:28:14.720 --> 00:28:17.917 >> I mean that's been that's been great to see there are there are many different 00:28:18.000 --> 00:28:21.757 models right if you look at the GitHub co-pilots of the world you know that's 00:28:21.840 --> 00:28:25.117 kind of complimentary to GitHub if you look at the cursors of the world. That's 00:28:25.200 --> 00:28:30.077 kind of an IDE model. If you look at the lovables and replets, that's more of a 00:28:30.160 --> 00:28:34.397 consumer model. It allows people who are not traditionally software developers to 00:28:34.480 --> 00:28:38.717 write code. Um, you know, there's like a, you know, Versell is like a different 00:28:38.800 --> 00:28:40.397 a different way of looking at it. 00:28:40.480 --> 00:28:44.557 Augment code, wind surf, you know, there's a whole ecosystem. And then 00:28:44.640 --> 00:28:46.477 there's the command line tools like like 00:28:46.560 --> 00:28:49.357 claude code. Uh, so I think it's too 00:28:49.440 --> 00:28:53.517 early to say, you know, what's going to be the winner there or or even if there 00:28:53.600 --> 00:28:56.477 is one winner, it's just possible there's just different things that work 00:28:56.560 --> 00:28:59.677 for different people or or different >> there's a whole civilization. There's a 00:28:59.760 --> 00:29:03.837 whole EOS capability, right? It's a whole civilization like it's an EO it's 00:29:03.920 --> 00:29:05.997 even it's broader like it's really 00:29:06.080 --> 00:29:08.557 coming across the whole span. It it's 00:29:08.640 --> 00:29:13.597 really impressive, you know? It's it's it's so fundamental that it's able to 00:29:13.680 --> 00:29:17.917 yield all this incredible innovation from others. Yeah. No, this I mean 00:29:18.000 --> 00:29:21.357 >> which one of those is the most like the one you did not expect? 00:29:21.440 --> 00:29:23.677 >> Which one is the one that I most did not 00:29:23.760 --> 00:29:28.477 expect? Um I think the model of kind of 00:29:28.560 --> 00:29:31.037 allowing people to write code end to end 00:29:31.120 --> 00:29:33.757 like allowing non-coders to write code. 00:29:33.840 --> 00:29:37.357 It totally makes sense once I see it and it's doing really well as a business 00:29:37.440 --> 00:29:40.957 model. But but I I I just when I was conceptualizing all the 00:29:41.040 --> 00:29:44.477 >> which product do you love the best? Uh yeah, this is this is one of those 00:29:44.560 --> 00:29:48.957 questions that you know you know c your customers are like your children. You 00:29:49.040 --> 00:29:51.677 you you you really shouldn't give an answer. So I'm not going to give an 00:29:51.760 --> 00:29:54.760 answer. 00:30:05.840 --> 00:30:07.597 All right. So are you getting a sense 00:30:07.680 --> 00:30:10.477 for Daario, by the way? Are you getting 00:30:10.560 --> 00:30:15.437 a sense for Daario? Okay. So that's I'm trying to like you know help them to 00:30:15.520 --> 00:30:19.597 understand you and how you're thinking about this and where we're going. So now 00:30:19.680 --> 00:30:21.437 here you are you know you're 00:30:21.520 --> 00:30:24.797 entrepreneur you've left open AI this 00:30:24.880 --> 00:30:26.717 thing is taking off. It's you and 00:30:26.800 --> 00:30:30.797 Daniela you know um you're in the Slack 00:30:30.880 --> 00:30:35.437 building you're get where is it going to be in 5 years? What 00:30:35.520 --> 00:30:39.677 does it look like? We've seen zero to five years now. You don't have to tell 00:30:39.760 --> 00:30:43.037 me as an investor and there's nobody hold going to hold you accountable for 00:30:43.120 --> 00:30:45.997 it. But just if you just fantasized out 00:30:46.080 --> 00:30:50.637 and now it's not 2025 but it's 2030 and 00:30:50.720 --> 00:30:55.277 you know where's Enthropic? What does it look like? Forget about all the other 00:30:55.360 --> 00:30:59.197 companies and all the other competitors whatever. Nobody cares. Where's 00:30:59.280 --> 00:31:00.957 Enthropic 5 years from now? What's your 00:31:01.040 --> 00:31:03.757 dream big what's your big dream? So, you 00:31:03.840 --> 00:31:07.997 know, I I think I think maybe maybe one of the best ways to think about this is, 00:31:08.080 --> 00:31:11.437 you know, I wrote this essay called Machines of Loving Grace where where 00:31:11.520 --> 00:31:14.317 it's really it was written from a societal perspective, but you know, of 00:31:14.400 --> 00:31:18.077 course, the company is trying to enact that and and and contribute to it and so 00:31:18.160 --> 00:31:22.157 you can back out from that where we where we hope the the company will be. 00:31:22.240 --> 00:31:25.437 So, you know, I I really thought of it as like the upside of AI, right? There's 00:31:25.520 --> 00:31:27.357 lots of concerns with AI. I I you know 00:31:27.440 --> 00:31:29.117 I'm one of the loudest in voicing them 00:31:29.200 --> 00:31:32.637 but but I am you know I am I am I am 00:31:32.720 --> 00:31:37.037 incredible believer in the upside of AI you know I think I think more so than 00:31:37.120 --> 00:31:38.957 people who code or describe themselves 00:31:39.040 --> 00:31:41.437 as more optimistic I have just a really 00:31:41.520 --> 00:31:44.637 expansive view I am perhaps most excited 00:31:44.720 --> 00:31:49.357 about what it can do for curing diseases um you know going back to my background 00:31:49.440 --> 00:31:51.517 in biology like you know we look at 00:31:51.600 --> 00:31:53.917 these tough diseases like cancer like 00:31:54.000 --> 00:31:56.957 Alzheimer's um you know like like you 00:31:57.040 --> 00:32:01.757 know various kinds of metabolic disease like I I I just think AI is the right 00:32:01.840 --> 00:32:04.957 thing to cure them and so I think of all the work we can do working with 00:32:05.040 --> 00:32:08.717 pharmaceutical companies working with scientific research institutions and 00:32:08.800 --> 00:32:12.397 working with the health care system to kind of enact it out and you know you 00:32:12.480 --> 00:32:15.517 again our partnership is about regulated industries and one of the regulated 00:32:15.600 --> 00:32:18.157 industries I think especially is is 00:32:18.240 --> 00:32:22.717 healthcare but you know that is one of the most positive applications but I 00:32:22.800 --> 00:32:26.397 think that we will see applications across the industry I'm very excited 00:32:26.480 --> 00:32:28.637 about financial services. I'm very 00:32:28.720 --> 00:32:31.197 excited about insurance. I'm very 00:32:31.280 --> 00:32:33.837 excited about taking basically every 00:32:33.920 --> 00:32:37.837 enterprise um and learning to serve 00:32:37.920 --> 00:32:41.517 every enterprise with smarter and smarter models. And the skill ceiling is 00:32:41.600 --> 00:32:45.437 very high. One way to say it is what is an enterprise? It's kind of a super 00:32:45.520 --> 00:32:47.517 intelligence, right? It it is an entity 00:32:47.600 --> 00:32:49.997 that acts in the world in in in in in in 00:32:50.080 --> 00:32:55.037 a way with a level of strategy and and knowledge and power in the world that 00:32:55.120 --> 00:32:58.957 goes far beyond what any individual human can do. So, you know, even as 00:32:59.040 --> 00:33:03.117 these models get smarter than than than any than any individual human, there's a 00:33:03.200 --> 00:33:07.197 ceiling substantially higher than that in terms of doing the things that a 00:33:07.280 --> 00:33:11.117 company tries to do in the world. Um, you know, most of which are good for the 00:33:11.200 --> 00:33:15.517 world, most of which create economic value. Um and and and so then my my 00:33:15.600 --> 00:33:19.277 vision of Enthropic is a company that serves the enterprise of the world that 00:33:19.360 --> 00:33:22.237 transforms every every enterprise puts 00:33:22.320 --> 00:33:24.797 AI at the center at the center of every 00:33:24.880 --> 00:33:27.277 enterprise. Um uh you know and and and 00:33:27.360 --> 00:33:29.517 and and you know I think I won't give 00:33:29.600 --> 00:33:31.997 specific numbers but like if you if you 00:33:32.080 --> 00:33:34.317 think about the actual market potential 00:33:34.400 --> 00:33:39.517 of that is very very high right our revenue has been going up 10x per year. 00:33:39.600 --> 00:33:42.317 It's currently, I think it was reported this morning, which I'm happy to confirm 00:33:42.400 --> 00:33:45.517 is correct, is approaching approaching 7 00:33:45.600 --> 00:33:50.557 billion run rate. Um, that's gone up 10x a year for the for the last 3 years. It 00:33:50.640 --> 00:33:54.397 can't continue like that for much longer, of course, but uh you know, 00:33:54.480 --> 00:33:57.917 >> as an investor, I'm looking forward to the $70 billion year next year. You 00:33:58.000 --> 00:34:00.717 should not feel bad investing at 4 bill. 00:34:00.800 --> 00:34:02.397 You should not feel bad. All right. 00:34:02.480 --> 00:34:06.637 >> Um uh uh uh so you know but I I I think 00:34:06.720 --> 00:34:10.397 the potential is is is very large. But I but I think even even more important 00:34:10.480 --> 00:34:15.277 than that or perhaps even necessary to that success, we want to be known for, 00:34:15.360 --> 00:34:19.197 you know, the company that is trustworthy, the company that behaves 00:34:19.280 --> 00:34:21.917 responsibly, the company that has a that 00:34:22.000 --> 00:34:24.957 has a reputation for, you know, doing 00:34:25.040 --> 00:34:29.357 what it says, saying what it does, and building products that are trustworthy 00:34:29.440 --> 00:34:33.117 that, you know, if you if you if you build on us, you won't get any 00:34:33.200 --> 00:34:35.677 surprises. You won't get anything crazy. 00:34:35.760 --> 00:34:38.717 You won't get the model, you know, saying it's Hitler. you won't you won't 00:34:38.800 --> 00:34:41.917 get, you know, all kinds of um you won't 00:34:42.000 --> 00:34:45.837 get all kinds of crazy stuff. Um and and I think that's very important to us 00:34:45.920 --> 00:34:48.877 being good stewards of this. >> Are there certain companies that you're 00:34:48.960 --> 00:34:50.557 trying to imply that that might be? 00:34:50.640 --> 00:34:53.757 >> None whatsoever. >> There's an entrepreneur down in Palo Alto. 00:34:53.840 --> 00:34:56.157 >> None at all. I don't know what you're talking about. 00:34:56.240 --> 00:35:00.077 >> Okay, I see. That's a certain letter in the alphabet. 00:35:00.160 --> 00:35:02.797 >> I don't I don't I I have no idea what you're talking about. Um 00:35:02.880 --> 00:35:06.077 >> Okay, that's before the letter Y. I 00:35:06.160 --> 00:35:08.557 sorry I still don't know what you mean. 00:35:08.640 --> 00:35:12.637 >> Okay, one more thing. I'm going to take a couple I'm going to use moderator 00:35:12.720 --> 00:35:13.917 privilege here and take a couple extra 00:35:14.000 --> 00:35:17.597 minutes with you. So number one, we see 00:35:17.680 --> 00:35:20.557 right now a tremendous fever on building 00:35:20.640 --> 00:35:24.237 data centers and you know we've seen a 00:35:24.320 --> 00:35:27.357 tremendous fever around the chips, the 00:35:27.440 --> 00:35:31.517 financing, the size and scale of the data centers, the energy of the data 00:35:31.600 --> 00:35:33.277 centers. you haven't really addressed it 00:35:33.360 --> 00:35:35.597 in your comments per se besides the 00:35:35.680 --> 00:35:41.117 scaling laws but let's talk about it just in part and parcel to enthropic and 00:35:41.200 --> 00:35:44.477 when you think about obviously one of your core partners has been Amazon it's 00:35:44.560 --> 00:35:49.837 been very important for your growth very important company um even though you do 00:35:49.920 --> 00:35:53.037 have you know obviously relationship with others as well but that one has 00:35:53.120 --> 00:35:57.197 been especially important to you know just give us a vision of what's 00:35:57.280 --> 00:36:00.877 happening with these data centers right now what are we going to see in the next 00:36:00.960 --> 00:36:04.317 few years with these data centers and how do you think about it as an 00:36:04.400 --> 00:36:08.477 entrepreneur who's going to need data centers to grow? Who will you go to? How 00:36:08.560 --> 00:36:10.237 will you do it? Will you build your own? 00:36:10.320 --> 00:36:13.517 Will you just continue to partner? What what what is your plan? 00:36:13.600 --> 00:36:17.037 >> Yeah. So, you know, my my my view is is 00:36:17.120 --> 00:36:21.277 that there is going to be an enormous need for compute for for all the reasons 00:36:21.360 --> 00:36:25.037 that uh you know that that we've laid out, right? The the scaling laws suggest 00:36:25.120 --> 00:36:27.837 that we're going to need more and more compute to train smarter and smarter 00:36:27.920 --> 00:36:31.197 models. And then as the revenue goes up, we're gonna we're, you know, we're going 00:36:31.280 --> 00:36:34.637 to need to serve more. We're going to need to serve more of our models. So 00:36:34.720 --> 00:36:39.757 when that revenue is not seven billion, but a hundred billion or more um uh and 00:36:39.840 --> 00:36:43.277 you know, when when model training requires millions of chips, you know, 00:36:43.360 --> 00:36:47.357 we're we're going to need many many gigawatts of power to to train the 00:36:47.440 --> 00:36:49.357 models. So we're trying to secure that 00:36:49.440 --> 00:36:54.397 power basically wherever we can. Um you know, and I'm confident in the company's 00:36:54.480 --> 00:36:55.997 uh uh I'm confident in the company's 00:36:56.080 --> 00:36:59.117 ability to do so. I I I do get a bit 00:36:59.200 --> 00:37:01.837 worried when the press focuses too much 00:37:01.920 --> 00:37:04.797 on the building of the data centers and 00:37:04.880 --> 00:37:09.117 deals to build data centers. Some of these deals seem a little bit fishy. 00:37:09.200 --> 00:37:13.037 They seem like they're maybe double counting. And I I also worry again 00:37:13.120 --> 00:37:14.557 >> deals are you ask talking about? 00:37:14.640 --> 00:37:15.677 >> I don't know what you're talking about. 00:37:15.760 --> 00:37:20.877 I have no idea. Um uh uh uh >> who is double counting? These are all 00:37:20.960 --> 00:37:24.557 public companies. I I >> We're going to want to know who's double counting. 00:37:24.640 --> 00:37:27.037 >> I don't I don't I don't know. I don't know anything more than you. 00:37:27.120 --> 00:37:28.557 >> What about the ones who are doing triple counting? 00:37:28.640 --> 00:37:32.157 >> I I don't There might be some of that, too. Wow. All right. 00:37:32.240 --> 00:37:35.517 >> I don't know. Um uh uh uh but but but 00:37:35.600 --> 00:37:38.077 look look look at the end of the day, 00:37:38.160 --> 00:37:40.317 all this data center stuff is spending 00:37:40.400 --> 00:37:42.717 money, right? Um I think the thing 00:37:42.800 --> 00:37:45.597 that's most important is making money. 00:37:45.680 --> 00:37:48.317 Um and and so this is why I focus on, 00:37:48.400 --> 00:37:50.717 you know, the revenue and the actual 00:37:50.800 --> 00:37:52.717 work that we're doing with businesses. 00:37:52.800 --> 00:37:57.277 Andropic is growing faster than any other company in the space. The revenue 00:37:57.360 --> 00:38:01.837 is growing faster. And you know, I think I think basically if if you know, if the 00:38:01.920 --> 00:38:05.117 if the demand is there, if customers want the money and they're willing to 00:38:05.200 --> 00:38:08.717 pay you, then you are willing to pay your comput providers and you'll get the 00:38:08.800 --> 00:38:11.117 compute you need at the end of the day. 00:38:11.200 --> 00:38:13.517 >> Daria, we could not be more grateful for 00:38:13.600 --> 00:38:18.557 you to be here, to be in San Francisco, to be a native son, and to Daniela as 00:38:18.640 --> 00:38:22.317 well, and for everything that you're doing with Enthropic. Congratulations. 00:38:22.400 --> 00:38:28.040 Please give Dario a round of applause.